Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car
Evaluation of driving behaviour is helpful for policy development, and for designing infrastructure and an intelligent safety system for a car. This study focused on a quantitative evaluation method of driving behaviour based on the shared-electrical car. The data were obtained from the OBD interfac...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/13/4625 |
| _version_ | 1797480306593234944 |
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| author | Shaobo Ji Ke Zhang Guohong Tian Zeting Yu Xin Lan Shibin Su Yong Cheng |
| author_facet | Shaobo Ji Ke Zhang Guohong Tian Zeting Yu Xin Lan Shibin Su Yong Cheng |
| author_sort | Shaobo Ji |
| collection | DOAJ |
| description | Evaluation of driving behaviour is helpful for policy development, and for designing infrastructure and an intelligent safety system for a car. This study focused on a quantitative evaluation method of driving behaviour based on the shared-electrical car. The data were obtained from the OBD interface via CAN bus and transferred to a server by 4G network. Eleven types of NDS data were selected as the indexes for driving behaviour evaluation. Kullback–Leibler divergence was calculated to confirm the minimum data quantity and ensure the effectiveness of the analysis. The distribution of the main driving behaviour parameters was compared and the change trend of the parameters was analysed in conjunction with car speed to identify the threshold for recognition of aberrant driving behaviour. The weights of indexes were confirmed by combining the analytic hierarchy process and entropy weight method. The scoring rule was confirmed according to the distribution of the indexes. A score-based evaluation method was proposed and verified by the driving behaviour data collected from randomly chosen drivers. |
| first_indexed | 2024-03-09T21:58:05Z |
| format | Article |
| id | doaj.art-dbdeb54095da49a6a802c28a06d0a925 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T21:58:05Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-dbdeb54095da49a6a802c28a06d0a9252023-11-23T19:54:48ZengMDPI AGEnergies1996-10732022-06-011513462510.3390/en15134625Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical CarShaobo Ji0Ke Zhang1Guohong Tian2Zeting Yu3Xin Lan4Shibin Su5Yong Cheng6College of Energy and Power Engineering, Shandong University, Jinan 250061, ChinaCollege of Energy and Power Engineering, Shandong University, Jinan 250061, ChinaDepartment of Mechanical Engineering Sciences, University of Surrey, Guildford GU2 7XH, UKCollege of Energy and Power Engineering, Shandong University, Jinan 250061, ChinaCollege of Energy and Power Engineering, Shandong University, Jinan 250061, ChinaResearch Management Department, Hisense TransTech Co., Ltd., Qingdao 266071, ChinaCollege of Energy and Power Engineering, Shandong University, Jinan 250061, ChinaEvaluation of driving behaviour is helpful for policy development, and for designing infrastructure and an intelligent safety system for a car. This study focused on a quantitative evaluation method of driving behaviour based on the shared-electrical car. The data were obtained from the OBD interface via CAN bus and transferred to a server by 4G network. Eleven types of NDS data were selected as the indexes for driving behaviour evaluation. Kullback–Leibler divergence was calculated to confirm the minimum data quantity and ensure the effectiveness of the analysis. The distribution of the main driving behaviour parameters was compared and the change trend of the parameters was analysed in conjunction with car speed to identify the threshold for recognition of aberrant driving behaviour. The weights of indexes were confirmed by combining the analytic hierarchy process and entropy weight method. The scoring rule was confirmed according to the distribution of the indexes. A score-based evaluation method was proposed and verified by the driving behaviour data collected from randomly chosen drivers.https://www.mdpi.com/1996-1073/15/13/4625driving behaviour evaluationnaturalistic driving studyshared-electrical carKullback–Leibler divergenceanalytic hierarchy processentropy weight method |
| spellingShingle | Shaobo Ji Ke Zhang Guohong Tian Zeting Yu Xin Lan Shibin Su Yong Cheng Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car Energies driving behaviour evaluation naturalistic driving study shared-electrical car Kullback–Leibler divergence analytic hierarchy process entropy weight method |
| title | Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car |
| title_full | Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car |
| title_fullStr | Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car |
| title_full_unstemmed | Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car |
| title_short | Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car |
| title_sort | evaluation method of naturalistic driving behaviour for shared electrical car |
| topic | driving behaviour evaluation naturalistic driving study shared-electrical car Kullback–Leibler divergence analytic hierarchy process entropy weight method |
| url | https://www.mdpi.com/1996-1073/15/13/4625 |
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